"was not well suited for the kind of hardware we're currently leveraging due to bad parallelization capabilities. \n",
"\n",
"Some extensions were provided by the academic community, such as Bidirectional RNN ([Schuster & Paliwal., 1997](https://www.researchgate.net/publication/3316656_Bidirectional_recurrent_neural_networks), [Graves & al., 2005](https://mediatum.ub.tum.de/doc/1290195/file.pdf)), \n",
"which can be seen as a concatenation of two sequential process, on going forward, the other one going backward over the sequence input.\n",
"which can be seen as a concatenation of two sequential process, one going forward, the other one going backward over the sequence input.\n",
"on translation tasks but it quickly extended to almost all the tasks RNNs were State-of-the-Art at that time.\n",
"\n",
"One advantage of Transformer over its RNN counterpart was its non sequential attention model. Remember, the RNNs had to\n",
"iterate over each element of the input sequence one-by-one and carry an \"updatable-state\" between each hop. With Transformer\n",
"the, the model is able to look at every position in the sequence, at the same time, in one operation.\n",
"iterate over each element of the input sequence one-by-one and carry an \"updatable-state\" between each hop. With Transformer, the model is able to look at every position in the sequence, at the same time, in one operation.\n",
"\n",
"For a deep-dive into the Transformer architecture, [The Annotated Transformer](https://nlp.seas.harvard.edu/2018/04/03/attention.html#encoder-and-decoder-stacks) \n",
"will drive you along all the details of the paper.\n",
...
...
@@ -68,7 +67,7 @@
"source": [
"## Getting started with transformers\n",
"\n",
"For the rest of this notebook, we will use a BERT model, as it's the most simple and there are plenty of content about it\n",
"For the rest of this notebook, we will use the [BERT (Devlin & al., 2018)](https://arxiv.org/abs/1810.04805) architecture, as it's the most simple and there are plenty of content about it\n",
"over the internet, it will be easy to dig more over this architecture if you want to.\n",
"\n",
"The transformers library allows you to benefits from large, pretrained language models without requiring a huge and costly computational\n",
@@ -11,7 +11,7 @@ Pull Request and we'll review it so it can be included here.
| Notebook | Description | |
|:----------|:-------------:|------:|
| [Getting Started Tokenizers](01-training_tokenizers.ipynb) | How to train and use your very own tokenizer |[](https://colab.research.google.com/github/huggingface/transformers/blob/docker-notebooks/notebooks/01-training-tokenizers.ipynb) |
| [Getting Started Tokenizers](01-training-tokenizers.ipynb) | How to train and use your very own tokenizer |[](https://colab.research.google.com/github/huggingface/transformers/blob/docker-notebooks/notebooks/01-training-tokenizers.ipynb) |
| [Getting Started Transformers](02-transformers.ipynb) | How to easily start using transformers | [](https://colab.research.google.com/github/huggingface/transformers/blob/docker-notebooks/notebooks/01-training-tokenizers.ipynb) |
| [How to use Pipelines](03-pipelines.ipynb) | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | [](https://colab.research.google.com/github/huggingface/transformers/blob/docker-notebooks/notebooks/01-training-tokenizers.ipynb) |
| [How to train a language model](https://github.com/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)| Highlight all the steps to effectively train Transformer model on custom data | [](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)|
Morgan Funtowicz <morgan@huggingface.co>